weight_cause_cox {cmprskcoxmsm} | R Documentation |
weight_cause_cox
fits the marginal structural proportional cause-specific hazards model using the inverse probability treatment weights.
weight_cause_cox(data=, time, time2 = NULL, Event.var, Event, weight.type, ties = NULL)
data |
The dataset, output of |
time |
See also |
time2 |
See also |
Event.var |
The variable name for the event indicator which typically has at least 3 levels. |
Event |
Event of interest, the rest of the event are treating as competing event. |
weight.type |
Type of inverse probability weights. Possible values are "Unstabilized" and "Stabilized". |
ties |
See also |
The marginal structural cause-specific Cox model for cause j usually has the form:
λ^{a}_j (t) \equiv λ_{T^{a},J^{a}=j}(t) = λ_{0j}e^{β*a},
where T^{a}, J^{a} is the counterfactural survival time and cause for treatment a (=0,1), λ_{0j} is the unspecified baseline cause-specific hazard for cause j, and β is the treatment effect.
Returns a table containing the estimated coefficient of the treatment effect, the robust standard error of the coefficient, estimated hazard ratio and 95% CI for the hazard ratio.